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Amod Jog
Researcher at Johns Hopkins University
Publications - 40
Citations - 1583
Amod Jog is an academic researcher from Johns Hopkins University. The author has contributed to research in topics: Image registration & Brain segmentation. The author has an hindex of 20, co-authored 40 publications receiving 1252 citations. Previous affiliations of Amod Jog include Harvard University & IBM.
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Journal ArticleDOI
Longitudinal multiple sclerosis lesion segmentation: Resource and challenge.
Aaron Carass,Snehashis Roy,Amod Jog,Jennifer L. Cuzzocreo,Elizabeth Magrath,Adrian Gherman,Julia Button,James Nguyen,Ferran Prados,Carole H. Sudre,Manuel Jorge Cardoso,Niamh Cawley,Olga Ciccarelli,Claudia A. M. Wheeler-Kingshott,Sebastien Ourselin,Laurence Catanese,Hrishikesh Deshpande,Pierre Maurel,Olivier Commowick,Christian Barillot,Xavier Tomas-Fernandez,Xavier Tomas-Fernandez,Simon K. Warfield,Simon K. Warfield,Suthirth Vaidya,Abhijith Chunduru,Ramanathan Muthuganapathy,Ganapathy Krishnamurthi,Andrew Jesson,Tal Arbel,Oskar Maier,Heinz Handels,Leonardo O. Iheme,Devrim Unay,Saurabh Jain,Diana M. Sima,Dirk Smeets,Mohsen Ghafoorian,Bram Platel,Ariel Birenbaum,Hayit Greenspan,Pierre-Louis Bazin,Peter A. Calabresi,Ciprian M. Crainiceanu,Lotta Maria Ellingsen,Lotta Maria Ellingsen,Daniel S. Reich,Jerry L. Prince,Dzung L. Pham +48 more
TL;DR: A quantitative evaluation comparing the consistency of the two raters as well as exploring the performance of the eleven submitted results in addition to three other lesion segmentation algorithms are presented.
Journal ArticleDOI
MRBrainS challenge: online evaluation framework for brain image segmentation in 3T MRI scans
Adriënne M. Mendrik,Koen L. Vincken,Hugo J. Kuijf,Marcel Breeuwer,Willem H. Bouvy,Jeroen de Bresser,Amir Alansary,Marleen de Bruijne,Aaron Carass,Ayman El-Baz,Amod Jog,Ranveer Katyal,Ali R. Khan,Fedde van der Lijn,Qaiser Mahmood,Ryan Mukherjee,Annegreet van Opbroek,Sahil Paneri,Sérgio Pereira,Mikael Persson,Martin Rajchl,Duygu Sarikaya,Örjan Smedby,Carlos A. Silva,Henri A. Vrooman,Saurabh Vyas,Chunliang Wang,Liang Zhao,Geert Jan Biessels,Max A. Viergever +29 more
TL;DR: The MRBrainS evaluation framework provides an objective and direct comparison of all evaluated algorithms and can aid in selecting the best performing method for the segmentation goal at hand.
Journal ArticleDOI
Random forest regression for magnetic resonance image synthesis.
TL;DR: An MRI image synthesis algorithm capable of synthesizing full‐head T2w images and FLAIR images and learns the nonlinear intensity mappings for synthesis using innovative features and a multi‐resolution design is described.
Proceedings ArticleDOI
Magnetic resonance image synthesis through patch regression
TL;DR: A data-driven approach to image synthesis is proposed, which provides equal, if not superior synthesis compared to the state-of-the-art, in addition to being an order of magnitude faster.
Journal ArticleDOI
MR image synthesis by contrast learning on neighborhood ensembles
TL;DR: An image synthesis approach that first estimates the pulse sequence parameters of the subject image to yield the particular target pulse sequence within the atlas is presented and superior in both intensity standardization and synthesis to other established methods is shown.